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Background: Akey process of neurodegeneration in Parkinson’s disease (PD) is the transneuronal spreading of a-synuclein. Alpha-synuclein is a presynaptic protein that is implicated in the pathogenesis of PD and other synucleinopathies, where it forms, upon intracellular aggregation, pathological inclusions.Other hallmarks of PD include neurodegeneration and microgliosis in susceptible brain regions. Whether it is primarilytransneuronal spreading of a-synuclein particles,inclusion formation, or other mechanisms, such as inflammation,that cause neurodegeneration in PDis unclear.Methods: We used spreading/aggregation ofa-synuclein induced by intracerebral injection of a-synucleinpreformed fibrils into the mouse brain to address this question. We performed quantitative histological analysis for a-synuclein inclusions, neurodegeneration, and microgliosis in different brain regions, and a gene expression profiling of the ventral midbrain, at two different timepoints after disease induction.Results: We observed significant neurodegeneration and microgliosis in brain regions not only with,but alsowithouta-synuclein inclusions. We also observed prominent microgliosis in injured brain regions that did not correlate with neurodegeneration nor with inclusion load. In longitudinal gene expression profiling experiments, we observedearly and unique alterationslinkedto microglial mediated inflammation that preceded neurodegeneration, indicating an active role of microglia in inducing neurodegeneration. Our observations indicate that a-synuclein inclusion formation is not the major driverin the early phases of PD-like neurodegeneration, but that diffusible, oligomeric a-synuclein species,which induce unusual microglial reactivity, play a key role in this process.Conclusion: Our findings uncover new features of a-synuclein induced pathologies, in particular microgliosis, and point to the necessity of a broader view of the process of “prion-like spreading” of that protein.

In the summer of 2021, European governments removed most NPIs after experiencing prolonged second and third waves of the COVID-19 pandemic. Most countries failed to achieve immunization rates high enough to avoid resurgence of the virus. Public health strategies for autumn and winter 2021 have ranged from countries aiming at low incidence by re-introducing NPIs to accepting high incidence levels. However, such high incidence strategies almost certainly lead to the very consequences that they seek to avoid: restrictions that harm people and economies. At high incidence, the important pandemic containment measure ‘test-trace-isolate-support’ becomes inefficient. At that point, the spread of SARS-CoV-2 and its numerous harmful consequences can likely only be controlled through restrictions. We argue that all European countries need to pursue a low incidence strategy in a coordinated manner. Such an endeavour can only be successful if it is built on open communication and trust., peer-reviewed

Real-world evaluations of metagenomic reconstructions are challenged by distinguishing reconstruction artifacts from genes and proteins present in situ. Here, we evaluate short-read-only, long-read-only and hybrid assembly approaches on four different metagenomic samples of varying complexity. We demonstrate how different assembly approaches affect gene and protein inference, which is particularly relevant for downstream functional analyses. For a human gut microbiome sample, we use complementary metatranscriptomic and metaproteomic data to assess the metagenomic data-based protein predictions. Our findings pave the way for critical assessments of metagenomic reconstructions. We propose a reference-independent solution, which exploits the synergistic effects of multi-omic data integration for the in situ study of microbiomes using long-read sequencing data.

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social measures will shape infections and hospitalizations. Hence, we extend the Susceptible-Exposed-Infectious-Removed (SEIR) model including these elements. We calibrate it to data of Luxembourg, Austria and Sweden until 15th December 2020. Sweden results having the highest fraction of undetected, Luxembourg of infected and all three being far from herd immunity in December. We quantify the level of social interaction, showing that a level around 1/3 of before the pandemic was still required in December to keep the effective reproduction number Refft below 1, for all three countries. Aiming to vaccinate the whole population within 1 year at constant rate would require on average 1,700 fully vaccinated people/day in Luxembourg, 24,000 in Austria and 28,000 in Sweden, and could lead to herd immunity only by mid summer. Herd immunity might not be reached in 2021 if too slow vaccines rollout speeds are employed. The model thus estimates which vaccination rates are too low to allow reaching herd immunity in 2021, depending on social interaction. Vaccination will considerably, but not immediately, help to curb the infection; thus limiting social interactions remains crucial for the months to come.

Background Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors including bacterial toxins contribute to pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is essential to identify virulence factors and antimicrobial resistance genes in situ. At present, there is a clear lack of computational approaches to simultaneously identify these factors in metagenomic datasets. Results Here, we present PathoFact, a tool for the contextualized prediction of virulence factors, bacterial toxins, and antimicrobial resistance genes with high accuracy (0.921, 0.832 and 0.979, respectively) and specificity (0.957, 0.989 and 0.994). We evaluate the performance of PathoFact on simulated metagenomic datasets and perform a comparison to two other general workflows for the analysis of metagenomic data. PathoFact outperforms all existing workflows in predicting virulence factors and toxin genes. It performs comparably to one pipeline regarding the prediction of antimicrobial resistance while outperforming the others. We further demonstrate the performance of PathoFact on three publicly available case-control metagenomic datasets representing an actual infection as well as chronic diseases in which either pathogenic potential or bacterial toxins are hypothesized to play a role. In each case, we identify virulence factors and AMR genes which differentiated between the case and control groups, thereby revealing novel gene associations with the studied diseases. Conclusion PathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, bacterial toxins, and antimicrobial resistance genes in metagenomic data. Additionally, our tool combines the prediction of these pathogenicity factors with the identification of mobile genetic elements. This provides further depth to the analysis by considering the genomic context of the pertinent genes. Furthermore, PathoFact’s modules for virulence factors, toxins, and antimicrobial resistance genes can be applied independently, thereby making it a flexible and versatile tool. PathoFact, its models, and databases are freely available at https://pathofact.lcsb.uni.lu.

This paper introduces a new lifting method for analyzing convergence of continuous-time distributed synchronization/consensus systems on the unit sphere. Points on the d-dimensional unit sphere are lifted to the (d+1)-dimensional Euclidean space. The consensus protocol on the unit sphere is the classical one, where agents move toward weighted averages of their neighbors in their respective tangent planes. Only local and relative state information is used. The directed interaction graph topologies are allowed to switch as a function of time. The dynamics of the lifted variables are governed by a nonlinear consensus protocol for which the weights contain ratios of the norms of state variables. We generalize previous convergence results for hemispheres. For a large class of consensus protocols defined for switching uniformly quasi-strongly connected time-varying graphs, we show that the consensus manifold is uniformly asymptotically stable relative to closed balls contained in a hemisphere. Compared to earlier projection based approaches used in this context such as the gnomonic projection, which is defined for hemispheres only, the lifting method applies globally. With that, the hope is that this method can be useful for future investigations on global convergence., Comment: 11 pages, 4 figures

Background High resolution mass spectrometry (HRMS) is being used increasingly in the context of suspect and non-targeted screening for the identification of bioorganic molecules. There is correspondingly increasing awareness that higher confidence identification will require a systematic, group effort to increase the fraction of compounds with tandem mass spectra available in central, publicly available resources. While typical suspect screening efforts will only result in tentative annotations with a moderate level of confidence, library spectral matches will yield higher confidence or even full confirmation of the identity if the reference standards are available. Results This article first explores representative percent coverage of measured tandem mass spectra in selected major environmental suspect databases of interest in the context of human biomonitoring, demonstrating the current extensive gap between the number of potential substances of interest (up to hundreds of thousands) and measured spectra (0.57–3.6% of the total chemicals have spectral information available). Furthermore, certain datasets are benchmarked, based on previous efforts, to show the extent to which acquired experimental data were comparable between laboratories, even with HRMS instruments based on different technologies (i.e., quadrupole–quadrupole-time of flight versus ion trap/quadrupole-Orbitrap). Instruments and settings that are less comparable are also revealed, primarily linear ion trap instruments, which show distinctly lower comparability. Conclusions Based on these efforts, harmonization guidelines for the acquisition and processing of tandem mass spectrometry data are proposed to enable European (and ideally worldwide) laboratories to contribute to common resources, without requiring extensive changes to their current in house methods.

The study of 3D cell culture models not only bridges the gap between traditional 2D in vitro experiments and in vivo animal models, it also addresses processes that cannot be recapitulated by these traditional models. Therefore, it offers an opportunity to better understand complex biology, for instance brain development, where conventional models have not proven successful. The so{called brain organoid technology provides a physiologically relevant context, which holds great potential for its application in modelling neurological diseases. To obtain these highly specialised structures, resembling specifically key features of the human midbrain, we derived a human midbrain-specific organoid (hMO) system from regionally patterned neural stem cells (NSCs). The resulting neural tissue exhibited abundant neurons with midbrain dopaminergic neuron (mDAN) identity, as well as astroglia and oligodendrocyte di erentiation. Within the hMOs, we could observe neurite myelination and the formation of synaptic connections. Regular fire patterning and neural network synchronicity were determined by multielectrode array (MEA) recordings. In addition to electrophysiologically functional mDANs producing and secreting dopamine (DA), we also detected responsive neuronal subtypes, like GABAergic and glutamatergic neurons. To investigate Parkinson's disease (PD)-relevant pathomechanisms, we derived hMOs from PD patients carrying the LRRK2-G2019S mutation and compared them to healthy control hMOs. In addition to a reduced number and complexity of mDANs, we determined a signi cant increase of the stem cell marker FOXA2 in the patient-derived hMOs. This suggests a neurodevelopmental defect induced by a PD-specific mutation and emphasises the importance of advanced three-dimensional (3D) stem cell-based in vitro models. The in this thesis described hMOs are suitable to reveal PD{relevant phenotypes, thus constitute as a powerful tool for human-specific in vitro disease modelling of neurological disorders with a great potential to be utilised in advanced therapy development.

By modulating the human gut microbiome, prebiotics and probiotics (combinations of which are called synbiotics) may be used to treat diseases such as colorectal cancer (CRC). Methodological limitations have prevented determining the potential combina- torial mechanisms of action of such regimens. We expanded our HuMiX gut-on-a-chip model to co-culture CRC-derived epithelial cells with a model probiotic under a simulated prebiotic regimen, and we integrated the multi-omic results with in silico metabolic modeling. In contrast to individual prebi- otic or probiotic treatments, the synbiotic regimen caused downregulation of genes involved in procarci- nogenic pathways and drug resistance, and reduced levels of the oncometabolite lactate. Distinct ratios of organic and short-chain fatty acids were produced during the simulated regimens. Treatment of primary CRC-derived cells with a molecular cocktail reflecting the synbiotic regimen attenuated self-renewal ca- pacity. Our integrated approach demonstrates the potential of modeling for rationally formulating synbi- otics-based treatments in the future.

While genetic advances have successfully defined part of the complexity in Parkinson’s disease (PD), the clinical characterization of phenotypes remains challenging. Therapeutic trials and cohort studies typically include patients with earlier disease stages and exclude comorbidities, thus ignoring a substantial part of the real-world PD population. To account for these limitations, we implemented the Luxembourg PD study as a comprehensive clinical, molecular and device-based approach including patients with typical PD and atypical parkinsonism, irrespective of their disease stage, age, comorbidities, or linguistic background. To provide a large, longitudinally followed, and deeply phenotyped set of patients and controls for clinical and fundamental research on PD, we implemented an open-source digital platform that can be harmonized with international PD cohort studies. Our interests also reflect Luxembourg-specific areas of PD research, including vision, gait, and cognition. This effort is flanked by comprehensive biosampling efforts assuring high quality and sustained availability of body liquids and tissue biopsies. We provide evidence for the feasibility of such a cohort program with deep phenotyping and high quality biosampling on parkinsonism in an environment with structural specificities and alert the international research community to our willingness to collaborate with other centers. The combination of advanced clinical phenotyping approaches including device-based assessment will create a comprehensive assessment of the disease and its variants, its interaction with comorbidities and its progression. We envision the Luxembourg Parkinson’s study as an important research platform for defining early diagnosis and progression markers that translate into stratified treatment approaches.

There is a global increase in the incidence of non-communicable diseases associated with unhealthy food intakes. Conditions such as diabetes, heart disease, high blood pressure, and strokes represent a high societal impact and an economic burden for health-care systems around the world. To understand these diseases, one needs to account the several factors that influence how the human body processes food, some of which are determined by the genome and patterns of gene expression that translate to the ability - or lack of - to degrade and absorb certain nutrients. Other factors, like the gut microbiota, are more volatile because its composition is highly moldable by diet and lifestyle. Multi-omics technologies can support the comprehensive collection of dietary intake data and monitoring of the health status of individuals. Also, a correct analysis of this data could lead to new insights about the complex processes involved in the digestion of dietary components and their involvement in the prevention or the appearance of health problems, but its integration and interpretation are still problematic. Thus, in this thesis, we propose the utilization of Constraint-Based Reconstruction and Analysis (COBRA) methods as a framework for the integration of this complex data. To achieve this goal, we have created a knowledge-base, the Virtual Metabolic Human (VMH), that combines information from large-scale models of metabolism from the human organism and typical gut microbes, with food composition information, and a disease compendium. VMH’s unique combination of resources leverages the exploration of metabolic pathways from different organisms, the inclusion of dietary information into in-silico experiments through its own diet designer tool, visualization and analysis of experimental and simulation data, and exploring disease mechanisms and potential treatment strategies. VMH is a step forward in providing the necessary tools to investigate the mechanisms behind the influence of diet in health and disease. Tools such as the diet designer can be used as a basis for diet optimization by predicting combinations of foods that can contribute to specific metabolic outcomes, which has the potential to be integrated and translated into treatment development and dietary recommendations in the foreseeable future.

The “dark matter” in metabolomics (unknowns) represents an exciting frontier with significant potential for discovery in relation to biochemistry, yet it also presents one of the largest challenges to overcome. This focussed review takes a close look at the current state-of-the-art and future challenges in tackling the unknowns with specific focus on the human gut microbiome and host-microbe interactions. Metabolomics, like metabolism itself, is a very dynamic discipline, with many workflows and methods under development, both in terms of chemical analysis and post-analysis data processing. Here, we look at developments in the mutli-omic analyses and the use of mass spectrometry to investigate the exchange of metabolites between the host and the microbiome as well as the environment within the microbiome. A case study using HuMiX, a microfluidics-based human-microbial co-culture system that enables the co-culture of human and microbial cells under controlled conditions, is used to highlight opportunities and current limitations. Common definitions, approaches, databases and elucidation techniques from both the environmental and metabolomics fields are covered, with perspectives on how to merge these, as the boundaries blur between the fields. While reflecting on the number of unknowns remaining to be conquered in typical complexsamples measured with mass spectrometry (often ordersof magnitude above the “knowns”), we provide an outlook on future perspectives and challenges in elucidating the relevant “dark matter”.

Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs. quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G x phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve (AUPR). By plotting the AUPR against the number of samples, we show that the tradeoff has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values on the ridge of the sigmoid.

The mammalian gene Nit1 (nitrilase-like protein 1) encodes a protein that is highly conserved in eukaryotes and is thought to act as a tumor suppressor. Despite being ∼35% sequence identical to ω-amidase (Nit2), the Nit1 protein does not hydrolyze efficiently α-ketoglutaramate (a known physiological substrate of Nit2), and its actual enzymatic function has so far remained a puzzle. In the present study, we demonstrate that both the mammalian Nit1 and its yeast ortholog are amidases highly active toward deaminated glutathione (dGSH; i.e., a form of glutathione in which the free amino group has been replaced by a carbonyl group). We further show that Nit1 -KO mutants of both human and yeast cells accumulate dGSH and the same compound is excreted in large amounts in the urine of Nit1 -KO mice. Finally, we show that several mammalian aminotransferases (transaminases), both cytosolic and mitochondrial, can form dGSH via a common (if slow) side-reaction and provide indirect evidence that transaminases are mainly responsible for dGSH formation in cultured mammalian cells. Altogether, these findings delineate a typical instance of metabolite repair, whereby the promiscuous activity of some abundant enzymes of primary metabolism leads to the formation of a useless and potentially harmful compound, which needs a suitable “repair enzyme” to be destroyed or reconverted into a useful metabolite. The need for a dGSH repair reaction does not appear to be limited to eukaryotes: We demonstrate that Nit1 homologs acting as excellent dGSH amidases also occur in Escherichia coli and other glutathione-producing bacteria.

Summary Parkinson’s disease (PD) exhibits systemic effects on the human metabolism, with emerging roles for the gut microbiome. Here, we integrate longitudinal metabolome data from 30 drug-naive, de novo PD patients and 30 matched controls with constraint-based modeling of gut microbial communities derived from an independent, drug-naive PD cohort, and prospective data from the general population. Our key results are (1) longitudinal trajectory of metabolites associated with the interconversion of methionine and cysteine via cystathionine differed between PD patients and controls; (2) dopaminergic medication showed strong lipidomic signatures; (3) taurine-conjugated bile acids correlated with the severity of motor symptoms, while low levels of sulfated taurolithocholate were associated with PD incidence in the general population; and (4) computational modeling predicted changes in sulfur metabolism, driven by A. muciniphila and B. wadsworthia, which is consistent with the changed metabolome. The multi-omics integration reveals PD-specific patterns in microbial-host sulfur co-metabolism that may contribute to PD severity., Graphical Abstract, Highlights • Longitudinal metabolomics reveal disturbed transsulfuration in Parkinson’s disease • Metabolic modeling of gut microbiomes show altered microbial sulfur metabolism • Changed microbial sulfur metabolism is linked to B. wadsworthia and A. muciniphila • Taurine-conjugated bile acids are associated with incident Parkinson’s disease, Hertel et al. demonstrate complex alterations in human and microbial sulfur metabolism in Parkinson’s disease by integrating longitudinal metabolomics and computational modeling of gut microbiomes. Then, potential clinical importance is revealed as secondary taurine-conjugated bile acids are shown to be associated with disease severity and Parkinson’s disease incidence.

Graphical abstract Quantitative multi-component analysis of body fluids with GC–MS. After sample collection, the samples are pre-processed and used for metabolite extraction. After this, GC–MS analysis and subsequent data processing and analysis are performed., Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples. Therefore, proper sample handling starting from the time of collection up to the analysis is crucial to obtain high quality samples and reproducible results. A metabolomics analysis is divided into 4 main steps: 1) Sample collection, 2) Metabolite extraction, 3) Data acquisition and 4) Data analysis. Here, we describe a protocol for gas chromatography coupled to mass spectrometry (GC–MS) based metabolic analysis for biological matrices, especially body fluids. This protocol can be applied on blood serum/plasma, saliva and cerebrospinal fluid (CSF) samples of humans and other vertebrates. It covers sample collection, sample pre-processing, metabolite extraction, GC–MS measurement and guidelines for the subsequent data analysis. Advantages of this protocol include: • Robust and reproducible metabolomics results, taking into account pre-analytical variations that may occur during the sampling process • Small sample volume required • Rapid and cost-effective processing of biological samples • Logistic regression based determination of biomarker signatures for in-depth data analysis

With technological advances in culture-independent molecular methods, we are uncovering a new facet of our natural history by accounting for the vast diversity of microbial life which colonizes the human body. The human microbiome contributes functional genes and metabolites which affect human physiology and are, therefore, considered an important factor for maintaining health. Much has been described in the past decade based primarily on 16S rRNA gene amplicon sequencing regarding the diversity, structure, stability and dynamics of human microbiota in their various body habitats, most notably within the gastrointestinal tract (GIT). Relatively high levels of variation have been described across different stages of life and geographical locations for the GIT microbiome. These observations may prove helpful for the future contextualization of patterns in other body habitats especially in relation to identifying generalizable trends over human lifetime. Given the large degree of complexity and variability, a key challenge will be how to define baseline healthy microbiomes and how to identify features which reflect deviations therefrom in the future. In this context, metagenomics and functional omics will likely play a central role as they will allow resolution of microbiome-conferred functionalities associated with health. Such information will be vital for formulating therapeutic interventions aimed at managing microbiota-mediated health particularly in the GIT over the course of a human lifetime.